Methods, defect review tools, and systems for locating a defect in a defect review process

ABSTRACT

Methods, defect review tools, and systems for locating a defect in a defect review process are provided. One method includes acquiring one or more images and data from an inspection tool. The one or more images illustrate an area on a specimen in which a defect to be reviewed is located. The data indicates a position and features of the defect within the area. The method also includes acquiring one or more additional images of the specimen proximate the position of the defect indicated in the data using an imaging subsystem of a defect review tool. In addition, the method includes identifying a portion of the one or more additional images that corresponds to the one or more images. The method further includes determining a position of the defect within the portion of the one or more additional images using the data.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to methods, defect review tools,and systems for locating a defect in a defect review process. Certainembodiments relate to determining a position of a defect on a specimenusing one or more images and data generated by an inspection tool andone or more additional images generated by an imaging subsystem of adefect review tool.

2. Description of the Related Art

The following description and examples are not admitted to be prior artby virtue of their inclusion in this section.

Inspection processes are used at various times during a semiconductormanufacturing process to detect defects on a specimen such as a reticleand a wafer. Inspection processes have always been an important part offabricating semiconductor devices such as integrated circuits. However,as the dimensions of semiconductor devices decrease, inspectionprocesses become even more important to the successful manufacture ofacceptable semiconductor devices. For instance, as the dimensions ofsemiconductor devices decrease, detection of defects of decreasing sizehas become necessary since even relatively small defects may causeunwanted aberrations in the semiconductor devices. Accordingly, muchwork in the inspection field has been devoted to designing inspectionsystems that can detect defects having sizes that were previouslynegligible.

Another important part of yield control is determining the cause of thedefects on the wafer or reticle such that the cause of the defects canbe corrected to thereby reduce the number of defects on other wafers orreticles. Often, determining the cause of the defects involvesidentifying the defect type and other characteristics of the defectssuch as size, shape, composition, etc. Since inspection typically onlyinvolves detecting defects on the wafer or reticle and providing limitedinformation about the defects such as location, number, and sometimessize, defect review is often used to determine more information aboutindividual defects than that which can be determined from inspectionresults. For instance, a defect review tool may be used to revisitdefects detected on a wafer or reticle and to examine the defectsfurther in some manner either automatically or manually. Defect reviewcan also be used to verify that defects detected by inspection areactual defects instead of, for example, noise and nuisance events.

Some examples of commonly used defect review tools include highresolution optical imaging systems, scanning electron microscopes (SEMS)and less commonly transmission electron microscopes. In order to besuccessful, the defect review tool must be able to accurately find thedefects that are to be reviewed. For instance, if the defect review tooldetermines an incorrect location for a defect, during review at theincorrect position the defect review tool field of view (FOV) may belocated on a non-defective portion of the specimen or a portion of aspecimen containing a different defect. In this manner, when the defectreview tool is erroneously positioned above a non-defective portion ofthe specimen, the defect review tool may determine that the detecteddefect is not an actual defect. Alternatively, if the defect review toolis erroneously positioned above a different defect on the specimen, thedefect review results for the reviewed defect may be assigned to thewrong detected defect. As such, if the defect review tool cannotaccurately find the defects that are to be reviewed, the defect reviewresults may be substantially inaccurate and without user intervention(e.g., manually reviewing the defect review results) such inaccuraciesmay be largely undetected. Therefore, if the results of defect revieware used to monitor and control semiconductor fabrication processes, themonitor and control may be ineffective or possibly even detrimental tothe performance of the semiconductor fabrication processes.

Finding the defects in a defect review process is not trivial for anumber of reasons. For instance, different coordinate sets are involvedin determining the coordinates of the defect with respect to the FOV ofthe defect review tool. One set of coordinates is the set of coordinatesin which the defect location is reported by the inspection tool thatdetected the defect. The defect location coordinates may be referencedto the inspection tool stage coordinates and translated such that thedefect coordinates are referenced to the center of the specimen. Anadditional set of coordinates is the set of coordinates of the specimenwith respect to the stage of the defect review tool. Since the specimenis typically moved from the inspection tool to the defect review tool,the coordinates of the specimen reported by the inspection tool willneed to be translated to the review tool stage coordinates. During thiscoordinate mapping, there are systematic and random errors that need tobe corrected before the review tool can locate the defect.

A number of different methods and systems have been developed to improvethe defect finding step of defect review processes. To eliminate thesystematic errors, normally wafer alignment, die corner registration,and defect coordinate deskew steps are performed. However, there arestill random errors in the defect coordinates that are caused by thedefect detection technology (such as technology for imaging orcollecting scattered light from the defect) and defect locationcalculation and interpolation. In addition, different thermal conditionsin the inspection tool during the defect detection and in the defectreview tool during defect review may produce random error in the defectcoordinates. One method to eliminate this random error that is commonlyreferred to as automatic defect locating (ADL) is performed using thedefect review subsystem of the defect review tool. ADL may includegenerating a low resolution image (or large FOV image) of a specimen atapproximately the location of a defect reported by the inspection toolusing an optical microscope (OM) or a SEM of a defect review tool. Thedefect may then be redetected in the low resolution image. For instance,the large FOV image can be compared to a reference image to detect thedefect in the low resolution image. A small FOV image of the defect maythen be captured which is commonly used for defect classificationpurposes.

Such methods have a number of disadvantages. For instance, the FOV onthe specimen during ADL is dependent on the defect coordinate inaccuracyof the inspection tool that inspected the specimen. In one such example,if the coordinates of the defect reported by the inspection tool aresubstantially inaccurate, then the FOV used for the low resolutionimaging may need to be large enough to ensure that the defect is locatedin the image, but so large that defect redetection (particularly forrelatively small defects) may not be possible or will take a relativelylong time to perform. In addition, the sensitivity of ADL is limited bythe parameters used for ADL including the FOV, pixel density, imageintegration time, noise in the image including specimen charging effectsin SEM images, and the redetection algorithm that is used. Furthermore,ADL performed using SEM images will not be able to redetect defects thatare inherently not visible to the SEM such as defects that are locatedbelow an upper surface of the specimen. Moreover, ADL performed usingSEM images may also have a low redetection success rate for other typesof defects such as low contrast defects, defects that are overwhelmed byunpredictable noise generated in the images, and defects that are toosmall to be redetected in low resolution SEM images.

An additional disadvantage of currently used methods and systems is thatduring ADL, imaging of the specimen by the SEM may cause contaminationof each area that is imaged (e.g., both a defect die and a referencedie). In addition, if all defect redetecting functions are performed inelectron beam mode, then such repeated exposure of the specimen to theelectron beam increases the potential for surface contamination anddamage. The specimen must also be imaged at least twice by the SEM: atleast once during ADL, and again during review. Therefore, such repeatedexposure to the electron beam may increase the probability of damage toand contamination of the specimen. Furthermore, in order to mitigate theeffects of ADL on the throughput of the defect review process, the lowresolution imaging by the SEM may be performed with relatively highcurrent and landing energy. Therefore, the portions of the specimenimaged during ADL may be subjected to conditions that are more likely tocause damage to and contamination of the specimen.

A different apparatus for reviewing defects on an object is illustratedin U.S. Pat. No. 6,407,373 to Dotan, which is incorporated by referenceas if fully set forth herein. As described by Dotan, the apparatusincludes a stage for receiving the object thereon, and both an OM and aSEM. The OM is used to redetect previously mapped defects on the objectsurface. Once the defect has been redetected, a translation system movesthe stage a predetermined displacement such that the defect ispositioned for review by the SEM.

Since the apparatus of Dotan does not use the SEM to redetect defects,the apparatus described by Dotan is less likely to cause electron damageand contamination of the specimen than the ADL methods described above.However, redetecting the defect as described by Dotan is disadvantageousfor a number of reasons. For instance, such redetection may be quickerthan the inspection process since only the positions in the defect mapproduced by inspection that indicate a potential defect are examined bythe OM of the defect review apparatus. However, the defects still haveto be redetected, which may limit the throughput, sensitivity, andaccuracy of the defect location of the defect review process.

The apparatus disclosed by Dotan also has some of the disadvantages ofADL described above. For instance, the FOV on the specimen during theredetection described by Dotan is dependent on the defect coordinateinaccuracy of the inspection tool that inspected the specimen, which isdisadvantageous as described further above. In addition, like ADL, thesensitivity of the defect redetection disclosed by Dotan is limited bythe parameters of the OM including the FOV, the pixel density, and theredetection algorithm. Furthermore, most of the time, the imaging modeof the defect review tool is substantially different from the imagingmode used by the inspection tool to detect the defects. As a result ofusing this ADL method, there is no guarantee that the redetected defectsin the FOV of the defect review tool are the same defects that arereported by the inspection tool. This error will cause false defectclassification and incorrect defect root cause analysis.

Accordingly, it may be advantageous to develop methods, defect reviewtools, and systems for locating a defect in a defect review process thatare independent of the defect coordinate accuracy of an inspection tool,are not limited by the parameters of the imaging subsystem used to imagethe specimen during defect locating, do not cause charging and/orcontamination of the specimen during defect locating, and do not reducethe throughput of the defect review process.

SUMMARY OF THE INVENTION

The following description of various embodiments of methods, defectreview tools, and systems is not to be construed in any way as limitingthe subject matter of the appended claims.

One embodiment relates to a method for locating a defect in a defectreview process. The method includes acquiring one or more images anddata from an inspection tool. The one or more images illustrate an areaon a specimen in which a defect to be reviewed is located. The dataindicates a position and features of the defect within the area. Themethod also include acquiring one or more additional images of thespecimen proximate the position of the defect indicated in the datausing an imaging subsystem of a defect review tool. In addition, themethod includes identifying a portion of the one or more additionalimages that corresponds to the one or more images. The method furtherincludes determining a position of the defect within the portion of theone or more additional images using the data.

In one embodiment, the method includes identifying the defect usinginformation within a database. The information was generated by theinspection tool. In another embodiment, the one or more images includeone or more bright field (BF) images, one or more dark field (DF)images, one or more laser DF images, one or more scanning electronmicroscope (SEM) images, or some combination thereof. In an additionalembodiment, the data includes one or more other images that illustratethe position and the features of the defect within the area. In someembodiments, the one or more additional images include one or more BFimages, one or more DF images, one or more laser DF images, one or moreSEM images, or some combination thereof.

In one embodiment, acquiring the one or more images and the data fromthe inspection tool is performed by the defect review tool. In anotherembodiment, the defect review tool and the inspection tool, incombination, are configured as an information on-demand system. In someembodiments, the imaging subsystem is configured as an opticalsubsystem. In an additional embodiment, the defect review tool isconfigured as a SEM.

In some embodiments, the method includes determining a position of thedefect with respect to the defect review tool from the position of thedefect within the portion of the one or more additional images such thatthe defect can be positioned within a field of view (FOV) of the defectreview tool. In another embodiment, the one or more images illustratepatterned features on the specimen. In one such embodiment, the dataincludes one or more other images that do not illustrate the patternedfeatures on the specimen. In a further embodiment, the identifying stepof the method includes comparing all patterned features illustrated inthe one or more images with patterned features illustrated in differentportions of the one or more additional images. In yet anotherembodiment, the identifying step includes comparing all pattern featuresand defect features illustrated in the one or more images with patternedfeatures and defect features illustrated in different portions of theone or more additional images.

In some embodiments, the identifying step includes determining ifmultiple portions of the one or more additional images correspond to theone or more images and comparing the multiple portions with each otherat the position indicated in the data to identify the multiple portionin which the defect is located. In another embodiment, the identifyingstep includes determining if multiple portions of the one or moreadditional images correspond to the one or more images. In one suchembodiment, the data includes one or more other images. In addition, theidentifying step includes comparing the multiple portions with the oneor more other images at the position of the defect illustrated in theone or more other images to identify the multiple portion in which thedefect is located.

In another embodiment, the method includes verifying the position of thedefect within the portion by acquiring one or more other images at theposition of the defect within the portion. In such an embodiment, animage type of the one or more additional images is different than animage type of the one or more other images. Each of the embodiments ofthe method described above may include any other step(s) describedherein.

Another embodiment relates to a defect review tool configured to locatea defect in a defect review process. The defect review tool includes aprocessor configured to acquire one or more images and data from aninspection tool. The one or more images illustrate an area on a specimenin which a defect to be reviewed is located. The data indicates aposition and features of the defect within the area. The defect reviewtool also includes an imaging subsystem configured to acquire one ormore additional images of the specimen proximate the position of thedefect indicated in the data. The processor is also configured toidentify a portion of the one or more additional images that correspondsto the one or more images and to determine a position of the defectwithin the portion of the one or more additional images using the data.

In one embodiment, the processor is configured to identify the defectusing information within a database. The information was generated bythe inspection tool. In another embodiment, the one or more imagesinclude one or more BF images, one or more DF images, one or more laserDF images, one or more SEM images, or some combination thereof. In anadditional embodiment, the data includes one or more other images thatillustrate the position and the features of the defect within the area.In some embodiments, the one or more additional images include one ormore BF images, one or more DF images, one or more laser DF images, oneor more SEM images, or some combination thereof.

In one embodiment, the defect review tool and the inspection tool, incombination, are configured as an information on-demand system. Inanother embodiment, the imaging system is configured as an opticalsubsystem. In some embodiments, the defect review tool includes a defectreview subsystem configured as a SEM. In a further embodiment, theprocessor is configured to detect a position of the defect with respectto a defect review subsystem from the position of the defect within theportion of the one or more additional images such that the defect can bepositioned within a FOV of the defect review subsystem.

In an additional embodiment, the one or more images illustrate patternedfeatures on the specimen. In one such embodiment, the data includes oneor more other images that do not illustrate the patterned features onthe specimen. In some embodiments, the processor is configured toidentify the portion of the one or more additional images thatcorresponds to the one or more images by comparing all patternedfeatures illustrated in the one or more images with patterned featuresillustrated in different portions of the one or more additional images.In other embodiments, the processor is configured to identify theportion of the one or more additional images that corresponds to the oneor more images by comparing all patterned features and defect featuresillustrated in the one or more images with patterned features and defectfeatures illustrated in different portions of the one or more additionalimages.

In one embodiment, the processor is configured to identify the portionof the one or more additional images that corresponds to the one or moreimages by determining if multiple portions of the one or more additionalimages correspond to the one or more images and comparing the multipleportions with each other at the position indicated in the data toidentify the multiple portion in which the defect is located. In anotherembodiment, the data includes one or more other images. In one suchembodiment, the processor is configured to identify the portion of theone or more additional images that corresponds to the one or more imagesby determining if multiple portions of the one or more additional imagescorrespond to the one or more images and comparing the multiple portionswith the one or more other images at the position illustrated in the oneor more other images to identify the multiple portion in which thedefect is located.

In one embodiment, the imaging subsystem is configured to acquire one ormore other images at the position of the defect within the portion. Inone such embodiment, an image type of the one or more additional imagesis different than an image type of the one or more other images. In suchembodiments, the processor may be configured to verify the position ofthe defect within the portion using the one or more other images. Eachof the embodiments of the system described above may be furtherconfigured as described herein.

An additional embodiment relates to a system configured to locate adefect in a defect review process. The system includes an inspectiontool configured to generate one or more images that illustrate an areaon the specimen in which a defect to be reviewed is located and datathat indicates a position and features of the defect within the area.The system also includes a defect review tool configured to acquire theone or more images and the data from the inspection tool. The defectreview tool is also configured to generate one or more additional imagesof the specimen proximate the position indicated in the data. Inaddition, the defect review tool is configured to identify a portion ofthe one or more additional images that corresponds to the one or moreimages. The defect review tool is further configured to determine aposition of the defect within the portion of the one or more additionalimages using the data. This system embodiment may be further configuredas described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Further advantages of the present invention may become apparent to thoseskilled in the art with the benefit of the following detaileddescription of the preferred embodiments and upon reference to theaccompanying drawings in which:

FIG. 1 is a flow chart illustrating one embodiment of a method forlocating a defect in a defect review process;

FIG. 2 includes schematic diagrams illustrating a top view of examplesof one or more images and data that may be acquired from an inspectiontool;

FIG. 3 is a schematic diagram illustrating a top view of an example ofone or more additional images that may be acquired using an imagingsubsystem of a defect review tool;

FIG. 4 is a schematic diagram illustrating a top view of an example of aportion of the one or more additional images of FIG. 3 that correspondsto the one or more images of FIG. 2;

FIG. 5 includes schematic diagrams illustrating a top view of oneexample of an image that may be acquired from an inspection tool and oneexample of a search window that may be located within an additionalimage that is acquired using an imaging subsystem of a defect reviewtool;

FIG. 6 is a schematic diagram illustrating one location of the imageacquired from the inspection tool of FIG. 5 within the search window ofFIG. 5;

FIG. 7 is a two-dimensional plot illustrating the grey levels of twoimages A and B at an initial location within the images;

FIG. 8 is a two-dimensional plot illustrating the grey levels of theimages A and B at additional locations within the images;

FIG. 9 is a three-dimensional plot illustrating the peak correlationvalues for the one or more images and one or more additional imagesshown in FIG. 4 across a search window;

FIG. 10 includes schematic diagrams illustrating a top view of examplesof one or more images and data that may be acquired from an inspectiontool, one or more additional images that may be acquired using animaging subsystem, and multiple portions of the one or more additionalimages that correspond to the one or more images;

FIG. 11 is a schematic diagram illustrating a side view of oneembodiment of a defect review tool configured to locate a defect in adefect review process and one embodiment of a system configured tolocate a defect in a defect review process;

FIG. 12 is a schematic diagram illustrating a side view of oneembodiment of an inspection tool that may be included in an embodimentof a system described herein; and

FIG. 13 is a schematic diagram illustrating a side view of oneembodiment of an imaging subsystem that may be included in an embodimentof a defect review tool described herein.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and may herein be described in detail. Thedrawings may not be to scale. It should be understood, however, that thedrawings and detailed description thereto are not intended to limit theinvention to the particular form disclosed, but on the contrary, theintention is to cover all modifications, equivalents and alternativesfalling within the spirit and scope of the present invention as definedby the appended claims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

As used herein, the term “specimen” refers to a reticle or a wafer. Theterms “reticle” and “mask” are used interchangeably herein. A reticlegenerally includes a transparent substrate such as glass, borosilicateglass, and fused silica having opaque regions formed thereon in apattern. The opaque regions may be replaced by regions etched into thetransparent substrate. Many different types of reticles are known in theart, and the term reticle as used herein is intended to encompass alltypes of reticles.

As used herein, the term “wafer” generally refers to substrates formedof a semiconductor or non-semiconductor material. Examples of such asemiconductor or non-semiconductor material include, but are not limitedto, monocrystalline silicon, gallium arsenide, and indium phosphide.Such substrates may be commonly found and/or processed in semiconductorfabrication facilities. A wafer may include one or more layers formedupon a substrate. For example, such layers may include, but are notlimited to, a resist, a dielectric material, and a conductive material.Many different types of such layers are known in the art, and the termwafer as used herein is intended to encompass a wafer including alltypes of such layers.

One or more layers formed on a wafer may be patterned or unpatterned.For example, a wafer may include a plurality of dies, each havingrepeatable pattern features. Formation and processing of such layers ofmaterial may ultimately result in completed devices. Many differenttypes of devices may be formed on a wafer, and the term wafer as usedherein is intended to encompass a wafer on which any type of deviceknown in the art is being fabricated.

Turning now to the drawings, it is noted that the figures are not drawnto scale. In particular, the scale of some of the elements of thefigures is greatly exaggerated to emphasize characteristics of theelements. It is also noted that the figures are not drawn to the samescale. Elements shown in more than one figure that may be similarlyconfigured have been indicated using the same reference numerals.

FIG. 1 illustrates one embodiment of a method for locating a defect in adefect review process. It is noted that all of the steps shown in FIG. 1are not essential to practice of the method. One or more steps may beomitted from or added to the method shown in FIG. 1, and the method canstill be practiced within the scope of the embodiments described herein.

As shown in step 10 of FIG. 1, the method includes acquiring one or moreimages (hereinafter “inspector image(s)”) and data (hereinafter“inspector data”) from an inspection tool. The inspector image(s)illustrate an area on a specimen in which a defect to be reviewed islocated. The inspector image(s) may include one or more bright field(BF) images, one or more dark field (DF) images, one or more laser DFimages, one or more scanning electron microscope (SEM) images, or somecombination thereof. The inspector data indicates a position andfeatures of the defect within the area. In some embodiments, theinspector data may include image data. In one such embodiment, theinspector data includes one or more other images (hereinafter“additional inspector image(s)”) that illustrate the position and thefeatures of the defect within the area.

FIG. 2 includes examples of such inspector image(s) and additionalinspector image(s). In particular, inspector image 12 is an example ofan image of a specimen that may be generated by an inspection toolduring inspection of the specimen. Inspector image 12 illustrates anarea on the specimen in which a defect to be reviewed is located. Thisspecimen is a wafer having patterned features formed thereon. Inspectorimage 12 illustrates the patterned features on the specimen. One exampleof an inspection tool that can generate such an inspector image is a BFinspection tool. As further shown in inspector image 12, the inspectorimage illustrates a relatively small portion of the specimen. Inparticular, the inspector image may be a “patch image” generated by theinspection tool. A “patch image” can be generally defined as an imagethat is “grabbed” by the inspection tool. In addition, the inspectorimage may be generated by the inspection tool during the inspection ofthe specimen (i.e. “run-time” images).

Additional inspector image 14 illustrates a position and features of adefect within the area defined by inspector image 12. In particular, thedefect appears as the bright spot in additional inspector image 14.Additional inspector image 14, in this example, does not illustrate thepatterned features on the specimen that are illustrated in inspectorimage 12. In particular, additional inspector image 14 may be adifference image that was generated by subtracting a reference imagefrom inspector image 12. The reference image may be another image of thespecimen generated by the inspection tool at a position on the specimenat which the features in inspector image 12 are also formed.Alternatively, the reference image may be an image of the specimengenerated (or “rendered”) from a database containing information aboutthe features formed on the specimen. In any case, the reference imagemay be subtracted from inspector image 12, and any differences betweenthe images may be used to detect defects on the specimen.

The inspector image(s) and inspector data may be acquired from aninspection tool in a number of different manners. In one embodiment,acquiring inspector image(s) and inspector data from the inspection toolis performed by the defect review tool that will perform the defectreview process. In another embodiment, the defect review tool and theinspection tool, in combination, are configured as an informationon-demand system, which is described in further detail herein. Forinstance, the defect review tool may send a request for the inspectorimage(s) and inspector data to the inspection tool. The defect reviewtool may then receive the requested inspector image(s) and inspectordata from the inspection tool. In addition, the defect review tool mayreceive only the inspector image(s) and inspector data that wererequested from the inspection tool. In this manner, the defect reviewtool may not receive all of the defect information and images of thespecimen that were generated during inspection. In addition to theinspector image(s) and inspector data, the defect review tool may alsorequest defect and other information from the inspection tool such asinspection optical mode, pixel size, illumination levels, defect sizes,and other defect features. Such requesting and receiving of defectinformation, inspector image(s), and inspector data may be performed inany manner known in the art.

Prior to requesting such inspector image(s) and inspector data from aninspection tool, the method may include determining which defects are tobe reviewed. For instance, the method may include identifying one ormore defects to be reviewed using information within a database. Theinformation in the database may have been generated by the inspectiontool. For instance, an inspection tool may be configured to generateinspection results in the form of a Klarity Results File (KLARF) or anyother file that can be created and read by multiple differentlyconfigured tools. The file may then be sent to a database such as adefect database or a fab database. The defect database may includeinspection results generated for a number of different specimens. Thedefect database may also include inspection results that were generatedby a number of different inspection tools. A fab database may includeinformation generated by a number of different tools in a fab (e.g.,inspection tools, metrology tools, defect review tools, process tools,etc.).

The inspection results may include a variety of information such as aspecimen identity and a defect list containing information about thedefects detected by the inspection tool. In this manner, upon receivingor determining the identity of the specimen on which defects are to bereviewed, the defect review tool may access the inspection results forthat specimen. In some embodiments, identifying the defect or defects tobe reviewed may include selecting the defects on the specimen that areto be reviewed (which is commonly referred to as “defect sampling”)using the inspection results from the database. Such defect sampling maybe performed by the inspection tool or the defect review tool. In thismanner, the inspector image(s), inspector data, and any otherinformation may be acquired for only the defects selected by sampling.

As shown in step 16 of FIG. 1, the method also includes acquiring one ormore additional images (hereinafter “review image(s)”) of the specimenproximate the position of the defect indicated in the inspector datausing an imaging subsystem of a defect review tool. In one embodiment,the imaging subsystem is configured as an optical subsystem. In anadditional embodiment, the imaging subsystem may be configured for BF,DF, or laser DF imaging. In another embodiment, the imaging subsystem isconfigured as a SEM. In some embodiments, the review image(s) includeone or more BF images, one or more DF images, one or more laser DFimages, one or more SEM images, or some combination thereof. The imagingsubsystem and the defect review tool may be configured as describedfurther herein.

FIG. 3 is one example of a review image of the specimen, for which theinspector image and inspector data are illustrated in FIG. 2. Reviewimage 18 may be acquired by an imaging subsystem that may beincorporated into a defect review tool. In one example, the imagingsubsystem that is used to acquire the review image(s) may be a BFoptical microscope (OM). Patterned features formed on the specimen areillustrated in review image 18. In addition, the review imageillustrates features across a much larger area on the specimen thaninspector image 12. In this manner, the review image may have beengenerated by an imaging subsystem that has a much larger field of view(FOV) than the FOV of the inspection tool which generated inspectorimage 12. In this manner, the review image may be a relatively lowresolution image of the specimen since such an image has sufficientresolution for the defect relocation steps described herein.

In one such example, the imaging subsystem may have a magnification thatis lower than that of the inspection tool. Otherwise, the imagingsubsystem and the inspection tool may be similarly configured. Forinstance, the imaging subsystems included in the defect review tool andthe inspection tool may have similar parameters such as wavelength(s) ofillumination, angle(s) of incidence, angle(s) of collection, detectorconfigurations, etc. Such similarities in the imaging subsystems mayreduce the potential for error in further steps of the method describedherein. However, the methods described herein may include accounting fordifferences in the imaging subsystems such as different pixel sizes anddifferent illumination wavelengths. In this manner, the methods may havea high degree of flexibility as to the different tools and subsystemsthat can be used to perform the methods.

In some embodiments, the method includes image pre-processing, as shownin step 20 of FIG. 1. Image pre-processing may include imagepre-processing of the inspector image(s) and the review image(s)acquired in steps 10 and 16, respectively, shown in FIG. 1. Imagepre-processing may include matching the pixel size of the inspector andreview images. In one embodiment, matching the pixel sizes of theinspector and review images may be performed using bilinearinterpolation for geometric scaling. In other embodiments, matching thepixel sizes of the inspector and review images may be performed usingbicubic interpolation, b-spline and other spline based interpolationtechniques, or any other suitable technique known in the art. Inaddition, the image pre-processing may include matching the grey levelsand contrast of the inspector and review images. Matching the greylevels and contrasts of the inspector and review images may includedetermining the mean and standard deviation of the selected inspectorimage or images, determining gain and offset values based on the meanand standard deviation, and applying the gain and offset values to theadditional image or images from the review tool to produce reviewimage(s) that have similar brightness and contrast as the inspectorimage(s). In another example, matching of the grey levels and contrastof the inspector and review images may include histogram matching,histogram percentile value matching, or any other suitable method oralgorithm known in the art.

As shown in step 22 of FIG. 1, the method also includes identifying aportion of the review image(s) that corresponds to the inspectorimage(s). For instance, as shown in FIG. 4, search window 24 may belocated within review image 18. Characteristics of the review imagewithin search window 24 and characteristics of inspector image 12 may becompared to determine if a portion of review image 18 within searchwindow 24 corresponds to the inspector image. The characteristics thatare compared may include characteristics of the features in the reviewimage within the search window and features illustrated in the inspectorimage.

In one embodiment, identifying the portion of the review image(s) thatcorresponds to the inspector image(s) includes comparing all patternedfeatures illustrated in the inspector image(s) with patterned featuresillustrated in different portions of the review image(s). In thismanner, all of the features within the inspector image(s) may be used inthe comparison such that the portion of the review image(s) thatcorresponds to the inspector image(s) is determined with relatively highaccuracy. In addition, all of the features within the inspector image(s)may be compared with features illustrated in different portions of thereview image(s) within the search window. In another embodiment,identifying the portion of the review image(s) that corresponds to theinspector image(s) includes comparing all patterned features and defectfeatures illustrated in the inspector image(s) with patterned featuresand defect features illustrated in different portions of the reviewimage(s). For example, if the defect is apparent in the inspectorimage(s), the defect in combination with other features in the inspectorimage(s) may be compared with features in the review image(s).Therefore, the defect is treated as a feature in the inspector image(s).In this manner, defect relocation involves pattern matching, notredetection of the defect, and the defect may be considered as part ofthe pattern that is matched.

As shown in FIG. 4, the dimensions of search window 24 are larger thanthe dimensions of inspector image 12 (taking into account thedifferences in the magnifications of the inspector and review images).The size of the search window may also directly correlate to the defectcoordinate inaccuracy of the inspection tool. Different inspection toolshave different expected defect coordinate inaccuracies. Therefore, thesize of the search window may be different for different inspectiontools. The error in the defect coordinate inaccuracy may also includeboth systematic and random errors of the inspection and review tools.The search window may also be larger at the beginning of the identifyingstep because at that point there are more uncertainties in the defectlocation. However, once at least some defect coordinates are correctedbased on the defects relocated by the review tool, the certainty of theremaining defect locations increases. Therefore, the dimensions of thesearch window may be reduced accordingly.

The position of the search window within the review image(s) during theidentification step may also vary. For instance, the position of thesearch window may be determined arbitrarily such that the search windowis located at the same position in each review image. In one suchexample, assuming that a review image is centered on the defectcoordinates reported by the inspection tool, the position of the searchwindow may be centered on the review image. Alternatively, the positionof the search window may be determined randomly. In another alternative,the position of the search window may be determined based oncharacteristics of the review image(s). For instance, the method mayinvolve searching the entire review image(s) for one or more featurescontained within the inspector image(s). In this manner, the method maybe able to roughly determine the probability that an inspector imagecorresponds to different portions of a review image and may eliminatethe low probability portions of the review image from considerationduring the identification step. In addition, the search window may bepositioned such that it contains a portion of the review image that hasa relatively high probability of corresponding to the inspector image.

In one embodiment, identifying the portion of the review image(s) thatcorresponds to the inspector image(s) as shown in step 22 of FIG. 1includes one or more steps such as registration of the review andinspector images, measuring a match between the review and inspectorimages, using edges of the review and inspector images for matching, andpeak isolation. Registration of the review and inspector images may beperformed to identify locations of the inspector image(s) that match thereview image(s). As shown in FIG. 5, the dimensions (e.g., the heightand the width) of search window 26 are larger than the dimensions (e.g.,the height and the width) of inspector image 28. As shown in FIG. 6,inspector image 28 may be positioned within search window 26. Inaddition, inspector image 28 may be moved (e.g., translated) to everypossible location in the search window. At each possible location of theinspector image within the search window, a match measurement may beperformed for the inspector image and the sub-image (i.e., that portionof the review image) that the inspector image overlaps. The matchmeasurements may be recorded in a two-dimensional plane (correspondingto the locations of the inspector image within the search window) and isoften referred to as a “correlation surface.” The better the matchbetween the inspection and review images at a location, the higher thematch measurement at that location. Hence, peaks in the correlationsurface correspond to good matches between the inspection and reviewimages.

In another example, given two images A and B having the same dimensionsor processed to have the same dimensions, measuring a match between theimages may include letting (g, g′) be the grey levels of images A and B,respectively, at location (0,0). This point may be plotted in a twodimensional plot such as the plot shown in FIG. 7. Additional suchpoints may be plotted that represent the grey levels of images A and Bat additional corresponding locations within the images. The grey levelsof the two images at all corresponding locations within the images maybe plotted. Such a plot may be generally referred to as a “scattergram.”The final plot generated by this step may appear similar to the plotshown in FIG. 8. How well images A and B match may then be determinedusing the properties of the scattergram. In particular, if images A andB match exactly, (e.g., grey level for grey level at every location),then the scatterplot may be a substantially perfect line having a 45°slope. There are many ways to perform matching using the scattergramdata. In one embodiment, the degree of matching may be determined usingnot only the linearity of the scattergram data but also the slope (wherea 45° slope represents an ideal match). This approach enables matchmeasurements to be more robust to noise and image distortions andprovides better quality matching peaks.

As mentioned above, edge images may be used for identifying matchesbetween inspector and review images. For example, to improve thereliability of the matching step, the edge or gradient-magnitude imagesof the inspector image(s) and the review image(s) may also be matched bythe process described above. To form the gradient image of a givenimage, the original image may be convolved with a gradient filter.Suitable gradient filters for such a step include the Sobel filter andany other gradient filter known in the art. Gradients have bothmagnitude and direction. The edge image is the gradient-magnitude image.

As further mentioned above, peak isolation may be used to identitymatches between the inspector image(s) and the review image(s). Forexample, multiple matches may be identified at different locations ofthe inspector images within the search window. In such an example, theexact locations of multiple matches may also be identified. In oneembodiment, all peaks in the correlation surface that have values abovea predetermined percentage of the globally maximum peak may be selectedas matches. In another embodiment, non-maximum suppression may be usedto set all values around a peak in the correlation surface to zerothereby aiding in identifying the locations of high peaks thatcorrespond to a match.

The identification of matches between the inspector image(s) and thereview image(s) may also include a number of further enhancements suchas weighting the correlation surface using a centrally symmetricflat-top Gaussian function such that locations that are distant from thecenter have a lower likelihood of being identified as a good match.

By comparing the inspector image(s) and the review image(s) as describedherein, the correlation of the inspector image(s) within the searchwindow of the review image(s) can be determined. As shown in the plot ofFIG. 9, single high correlation peak 30 indicates the position withinthe search window having the best correlation with the inspectorimage(s). As shown in FIG. 4, this high correlation peak corresponds toportion 32 of review image 18 within search window 24 that is determinedto correspond to inspector image 12. Even if a perfect match of theinspector image is found in the review image, normal patterns of thespecimen and the defect cannot be distinguished from one another. Inthis manner, additional inspector image 14 shown in FIG. 2, which is thedifference image corresponding to inspector image 12, is used toidentify the position of the defect within the portion of the reviewimage that matches the inspector image. In this manner, the defectlocation can be derived from the additional inspector image(s) after thebest correlation of the inspector and the review images has beenidentified.

The method, therefore, includes determining a position of the defectwithin the portion of the review image(s) using the inspector data. Forinstance, after portion 32 of review image 18 has been identified, themethod may include overlaying additional inspector image 14 (shown inFIG. 2) with portion 32 of review image 18. In this manner, the positionat which the bright spot corresponding to the defect shown in additionalinspector image 14 overlaps with portion 32 of review image 18 indicatesthe location of the defect within portion 32. In another example, themethod may include determining the coordinates of the bright spot withinadditional inspector image 14. In this manner, after portion 32 ofreview image 18 has been identified, the method may include identifyingthe coordinates within the portion of the review image that correspondto the coordinates of the bright spot within additional inspector image14 as the location of the defect within the portion of the review image.As described above, therefore, the method determines the position of thedefect within the review image(s), not by redetecting the defect as inpreviously used methods, but from the position of the defect ininspector data. In this manner, the defect does not have to be imaged inthe review image(s) for redetection purposes.

As shown in step 34 of FIG. 1, the method may include determining if asingle portion of the review image(s) was determined to correspond tothe inspector image(s). If a single portion of the review image(s) wasdetermined to correspond to the inspector image(s), the method mayinclude locating the defect on the review tool as shown in step 36 ofFIG. 1, which may be performed as described herein. One example in whicha single solution is identified by determining a portion of reviewimage(s) that corresponds to inspector image(s) is illustrated in theembodiments described above with respect to the images shown in FIGS.2-4 since only one portion of review image 18 is determined tocorrespond to the inspector image. However, when a specimen containsrepeatable pattern features, it may be possible that multiple portionsof the review image(s) may be determined to correspond to inspectorimage(s).

In one embodiment, identifying a portion of review image(s) thatcorresponds to the inspector image(s) includes determining if multipleportions of the review image(s) correspond to the inspector image(s). Inthis manner, after finding one portion of the review image(s) thatcorresponds to the inspector image(s), the identification step is notterminated. Instead, all portions of the review image(s) are evaluatedto determine if more than one portion corresponds to the inspectorimage(s). The multiple portions may be determined to correspond to theinspector image(s) if they have a correlation peak that is above somepredetermined value (e.g., greater than about 0.6) and/or meet somecriteria (e.g., location close to the expected defect location, etc.)

In one example of such an embodiment, FIG. 10 illustrates examples ofinspector image 38 and additional inspector image 40, which may beacquired as described herein. Identifying a portion of review image 42that corresponds to the inspector image was performed as describedabove. For instance, such an identifying step may include patternmatching of the patch image from the inspection tool with the imagingsubsystem image. As shown in review image 42, multiple portions 44 weredetermined to correspond to inspector image 38. In this manner, multipleidentically matched patterns are identified within the search window ofthe review image, and each portion of the review image that matched theinspector image may be defined as a “block.”

As shown in review image 42, five multiple portions were determined tocorrespond to the inspector image. However, in other instances, two ormore multiple portions may be determined to correspond to the inspectorimage. In addition, although one particular arrangement of the multipleportions is shown in FIG. 10, it is to be understood that the multipleportions may be located at any positions within the review image. Sincemultiple portions of review image 42 were determined to correspond tothe inspector image, it is desirable to determine the multiple portionin which the defect is located. In other words, it will be desirable toidentify which of the multiple portions actually corresponds to theinspector image(s). Therefore, as shown in step 46 of FIG. 1, whenmultiple portions of the review image(s) were determined to correspondto the inspector image(s), the method may include evaluating themultiple corresponding portions of the review image(s) to determine thedefective portion.

In one such embodiment, the method includes comparing the multipleportions with each other at the position of the defect indicated in theinspector data to identify the multiple portion in which the defect islocated. In other words, the multiple portions may be compared with eachother at the position within the multiple portions corresponding to thedefect in a block-to-block fashion to determine which of the multipleportions is different than the others. In this manner, block-to-blockdefect relocation may be performed by focusing only on the defectivepixel area in the portions of the inspector data or additional inspectorimage(s). The multiple portion that is different from the others is thendetermined as the multiple portion containing the defect.

In another embodiment, the position of each multiple portion in thereview image(s) may be used to determine the position of each multipleportion with respect to the defect review subsystem. An additionalreview image that contains all of the identical multiple portions maythen be generated by the defect review subsystem (e.g., with highresolution and small FOV). Block-to-block comparison of the additionalreview images generated by the defect review subsystem may then beperformed as described above to determine the multiple portion in whichthe defect is located. In a different embodiment, the block-to-blockcomparison may be performed using high resolution, small FOV images ofthe multiple portions generated by the imaging subsystem instead of thedefect review subsystem.

Comparing the multiple portions to each other to identify the multipleportion in which the defect is located will be successful when three ormore multiple portions have been found. However, when only two multipleportions have been identified, the multiple portion in which the defectis located cannot be determined from a comparison between the twomultiple portions. In particular, both of the multiple portions will bedifferent from each other (since only one contains the defect andtherefore the other does not). In this case, the inspector data andpossibly other defect information that was requested from the inspectiontool may be used to determine which multiple portion contains thedefect. For example, the defect information may indicate that the defecthas a higher grey level than the background. In such an example, thedefect appears brighter than the background. As such, the defect pixelsof these two multiple portions may be compared, and the multiple portionthat shows brighter pixels at the position corresponding to the defectcompared to the background is determined as the block in which thedefect is located. In another embodiment, because the grey level andcontrast of the optical images are directly linked to the illuminationwavelength and illumination mode, the method may include normalizing thegrey levels and contrast of the inspector image(s) and review image(s)before using the defect grey level and contrast information to determinethe block containing the defect.

As described above, the inspector data may include one or moreadditional inspector images. In one such embodiment, the methodembodiments described herein include comparing the multiple portionswith the additional inspector image(s) at the position of the defectillustrated in the additional inspector image(s) to identify themultiple portion in which the defect is located. For instance, thecharacteristics of the multiple portions at the position of the defectmay be compared to the characteristics of the inspector data at theposition of the defect. The multiple portion that has characteristics atthe position of the defect that are most similar to the characteristicsof the inspector data may be determined to be the multiple portion inwhich the defect is located. For instance, as shown in FIG. 2, inadditional inspector image 14, the defect appears as a relatively brightspot. Therefore, the multiple portion that also contains a relativelybright spot at the corresponding position may be determined to be themultiple portion in which the defect is located. In this embodiment, themultiple portion images that are compared to the inspector data may below resolution images or high resolution images. High resolution imagesof the multiple portions may be generated by the imaging subsystem orthe defect review subsystem.

In another embodiment, the method includes verifying the position of thedefect within the portion by acquiring one or more other images (i.e.,additional review) image(s)) at the position of the defect within theportion. An image type of the review image(s) is different than an imagetype of the additional review images. For example, the review image(s)may be optical image(s), and the additional review image(s) may be SEMimage(s). In another example, the review image(s) may be large FOVimage(s), and the additional review image(s) may be small FOV images.The review image(s) may be acquired by the imaging subsystem, and theadditional review image(s) may be acquired by the imaging subsystem orthe defect review subsystem.

The method embodiments described above involve determining a “local”position of the defect (i.e., a position of the defect within the reviewimage). However, the position of the defect with respect to the FOV ofthe defect review subsystem may also be determined. In one embodiment,therefore, the method includes determining a position of the defect withrespect to the defect review tool from the position of the defect withinthe portion of the review image(s) such that the defect can bepositioned within a FOV of the defect review tool.

In one such embodiment, the position of the specimen with respect to theimaging subsystem may be determined when the specimen is disposed upon astage coupled to the imaging subsystem (e.g., prior to defectrelocation). Therefore, the position of the review image of the specimencan be determined with respect to the specimen as a whole or withrespect to the imaging subsystem based on the position of the specimenwith respect to the imaging subsystem. In this manner, the position ofthe defect can be determined with respect to the specimen or withrespect to the imaging subsystem.

This positional information can then be used to determine a position ofthe defect with respect to a FOV of the defect review subsystem. Forinstance, the positional relationship between the imaging subsystem andthe defect review subsystem may be predetermined (e.g., by priormeasurements and/or calibration), and the position of the defect withrespect to the imaging subsystem can be used to determine the positionof the defect with respect to the FOV of the defect review subsystembased on this positional relationship. In another instance, after defectrelocation, the defect review subsystem or a processor coupled to thedefect review subsystem may be configured to determine a position of thespecimen with respect to the defect review subsystem. Such a positionalrelationship may be used to align the specimen with respect to thedefect review subsystem or to account for variations in the specimenposition after movement from the imaging subsystem FOV to the defectreview subsystem FOV. In this manner, the position of the defect withrespect to the specimen and this positional relationship can be used todetermine the position of the defect with respect to the FOV of thedefect review subsystem.

Although the method embodiments are described herein with respect to adefect to be reviewed, it is to be understood that the methods mayinclude relocating more than one defect on a specimen. In addition, thedefect relocation results for one or more defects may be used incombination with defect coordinates reported by the inspection tool todetermine the position of other defects on the specimen with respect tothe defect review subsystem. Furthermore, the method embodiments mayinclude any other step(s) described herein.

Unlike previously used methods and systems for automatic defect location(ADL), which are described further in the “Description of the RelatedArt” section above, the embodiments of the methods described herein donot include redetecting the defects during the defect review process.Instead, the position of the defect is “relocated” by correlatinginspector image(s) with a portion of review image(s). The correlationdoes not involve matching only the defect in the images. Instead, theimages are correlated by using additional information about the specimento accurately determine the position of the defect within reviewimage(s) in both the x and y dimensions. In addition, by usinginformation that has characteristics in the x and y dimensions, anyvariation in the angle of rotation of the specimen between inspectionand defect review may be detected and corrected. Therefore, the methodsdescribed herein can be used to detect and correct any type of spatialvariation in the defect location between the inspection tool and thedefect review tool.

Accordingly, the method embodiments described herein have a number ofadvantages over the previously used methods. For example, the accuracyof the embodiments of the methods described herein is independent of thedefect coordinate accuracy of an inspection tool. In particular, sincethe methods described herein include correlating features of inspectorimage(s) with features of review image(s), the success of the defectrelocation is not dependent on the coordinates of the defect reported bythe inspection tool. More specifically, the FOV of the imaging subsystemof the defect review tool can be adjusted based on the expectedinaccuracy of the reported coordinates from the inspection tool, andsince the defect is not redetected in the methods described herein, theeffect of the FOV on the review image(s) of the defect will not affectthe accuracy of the method embodiments. In addition, the methodsdescribed herein involve close coupling of information and imagesbetween the inspection tool and the defect review tool.

The method embodiments described herein are, therefore, more accuratefor defect relocation during a defect review process than previouslyused methods. For instance, the method embodiments described herein arecapable of substantially higher ADL success rates with high confidencethat the located defects are actually the defects reported by theinspection tool. In contrast, previously used methods use reviewimage(s) to redetect defects, and many times defects different from theones that are reported by the inspection tool are detected on the reviewtool.

Furthermore, the methods described herein are also capable of relocatingdefects that cannot be imaged by electron beam based defect reviewsubsystems such as defects that are located below the upper surface ofthe specimen (e.g., previous layer defects or completely subsurfacedefects such as voids and inclusions). In addition, if the successfullyrelocated defects are not imaged by an electron beam based defect reviewsubsystem, then the relocated defects may be classified as previouslayer or non-surface defects. Such relocation and classification ofdefects that are not visible in SEM images is important such that thedefects can be examined in other tools such as a focused ion beam (FIB)tool or other failure analysis tool.

The methods described herein are also capable of relocating defect typesthat may be difficult to relocate successfully using SEM images such aslow contrast defects, defects that exhibit signals that may beoverwhelmed by unpredictable noise, defects that are too small to beimaged in low resolution SEM images, and defects that are reported dueto color variations. In particular, since the methods described hereinutilize background pattern information instead of or in addition todefect information to perform defect relocation, the methods are notlimited by defect types, defect sizes, and defects that are not visibleto a SEM.

Moreover, the method embodiments described herein are not limited by theparameters of the imaging subsystem used to image the specimen duringdefect locating. In particular, as described above unlike previouslyused methods for defect redetection, an image of the defect itself isnot required to relocate the defect during defect review. As such, themethod embodiments described herein do not depend on the ability of theimaging subsystem to image a defect. Such independence from the defectimaging ability of the imaging subsystem is particularly advantageousfor defects that are difficult to image such as low contrast defects. Inaddition, since the method embodiments described herein do not depend onredetection of the defect, the method embodiments described herein arenot limited by other parameters involved in defect redetection such asdata processing parameters. Therefore, the method embodiments describedherein are capable of relocating a defect during a defect review processwith higher accuracy than previously used methods.

A further advantage of the embodiments described herein is that sincethe methods can use an optical imaging subsystem to locate a defect in adefect review process, these methods do not cause charging and/orcontamination of the specimen during defect locating. In particular,using an optical imaging subsystem instead of an electron beam subsystemto determine a location of a defect prevents carbonization ofcontamination already present on the specimen. In addition, since arelatively large FOV optical imaging subsystem can be used to performdefect relocation, the specimen does not have to be subjected toexposure to an electron beam twice: once during defect relocation andagain during defect review as in previously used methods. Furthermore,since defect relocation using an electron beam subsystem typicallyincludes exposing the specimen to a relatively high landing energy andbeam current such that a large FOV on the specimen can be imaged withoutsubstantially reducing the throughput of the defect review process, theelectron beam exposure of the specimen that has the largest potentialfor damaging the specimen is eliminated by the methods described herein.Since the exposure of the specimen to the electron beam during actualdefect review is typically performed with a much lower landing energyand beam current (since a much smaller FOV image is generated), damageof the specimen due to defect review processes is substantiallyeliminated (or at least minimized) by the method embodiments describedherein.

The method embodiments described herein also do not reduce thethroughput of the defect review process. For instance, all of the stepsof the method embodiments described herein can be performed relativelyquickly. In particular, all of the steps of the method embodimentsexcept for the acquisition of the review image(s) by the imagingsubsystem involve data processing. The throughput of the data processingsteps is only limited by the performance of the components used toperform the data processing. In addition, since the acquisition of thereview image(s) by the imaging subsystem may involve optical imaging,this step of the method embodiments also has a relatively highthroughput. Furthermore, since the methods described herein do notinvolve defect redetection, the methods described herein eliminate anumber of imaging and processing steps that might otherwise reduce thethroughput of the defect review process. Moreover, the methods describedherein can be completely automated. Such automation also contributes tothe high throughput and ease of use of the methods described herein.

Program instructions implementing methods such as those described hereinmay be transmitted over or stored on a carrier medium. The carriermedium may be a transmission medium such as a wire, cable, or wirelesstransmission link. The carrier medium may also be a storage medium suchas a read-only memory, a random access memory, a magnetic or imageacquisition disk, or a magnetic tape.

The program instructions may be implemented in any of various ways,including procedure-based techniques, component-based techniques, and/orobject-oriented techniques, among others. For example, the programinstructions may be implemented using Matlab, Visual Basic, ActiveXcontrols, C, C++ objects, C#, JavaBeans, Microsoft Foundation Classes(“MFC”), or other technologies or methodologies, as desired.

The program instructions may be executable on a processor that may beincluded in a computer system. The computer system may take variousforms, including a personal computer system, mainframe computer system,workstation, image computer or any other device known in the art. Ingeneral, the term “computer system” may be broadly defined to encompassany device having one or more processors, which executes instructionsfrom a memory medium. The processor may be further configured asdescribed herein.

Another embodiment relates to a defect review tool that is configured tolocate a defect in a defect review process. As will be obvious based onthe description of the defect review tool embodiments provided herein,the defect review tool embodiments have all of the advantages of themethod embodiments described above. One embodiment of such a defectreview tool is illustrated in FIG. 11. As shown in FIG. 11, defectreview tool 48 includes processor 50. Processor 50 is configured toacquire inspector image(s) and inspector data from inspection tool 52.The inspector image(s) illustrate an area on a specimen in which adefect to be reviewed is located. The inspector image(s) may include oneor more BF images, one or more DF images, one or more laser DF images,one or more SEM images, or some combination thereof. The inspector dataindicates a position and features of the defect within the area. Theinspector data may include one or more additional inspector image(s)that illustrate the position and the features of the defect within thearea. The inspector image(s) and inspector data may be any other imagesdescribed further herein. For instance, in one embodiment, the inspectorimage(s) illustrate patterned features on the specimen, and theinspector data includes additional inspector image(s) that do notillustrate the patterned features on the specimen.

Processor 50 may be configured to acquire the inspector image(s) andinspector data from inspection tool 52 in a number of ways. For example,processor 50 may be coupled to one or more components of inspection tool52 by a transmission medium (not shown). The transmission medium mayinclude any suitable transmission medium known in the art and mayinclude “wired” and “wireless” transmission media. The one or morecomponents of inspection tool 52 to which processor 50 are coupled mayinclude a processor and/or a storage medium (not shown) of inspectiontool 52. In one such embodiment, processor 50 may send a request for theinspector image(s) and inspector data to a processor of inspection tool52. The processor of the inspection tool may then retrieve the inspectorimage(s) and inspector data from a storage medium of the inspectiontool. The processor of the inspection tool may also send the inspectorimage(s) and inspector data to processor 50. In this manner, processor50 may receive inspector image(s) and inspector data from the inspectiontool across the transmission medium, which functions as a data stream.In addition, processor 50 may receive only the inspector image(s) andinspector data that are requested. As such, processor 50 may not receiveall of the images that were generated by the inspection tool duringinspection of the specimen. Processor 50 may also request and receivedefect and other information such as that described herein in a similarmanner.

In this manner, the defect review tool and the inspection tool, incombination, may be configured as an information on-demand system. Inother words, during the defect review process run time, the defectreview tool sends a request to the inspection tool for specific defectinformation (e.g., defect patch images, features, polarity, size,region, etc.). The inspection tool may also be configured to send arequest to the defect review tool for information that can be used toimprove performance of the inspection tool (e.g., defect reviewsubsystem images of the defects, material information of the defects,etc.). Examples of inspection system setup techniques are illustrated inU.S. Patent Application Publication No. 2004/0038454 to Coldren et al.,which is incorporated by reference as if fully set forth herein. Themethods described herein may include any step(s) described in thispublication. In addition, the systems described further herein may befurther configured as described in this publication.

In some embodiments, processor 50 is configured to identify the defectto be reviewed using information within database 54. The information wasgenerated by inspection tool 52. In this manner, the inspection toolgenerated information in database 54 that can be accessed by a processorcoupled to a defect review tool. Database 54 may be configured as adefect database. In this manner, the defect database may includeinspection results from inspection tool 52 and possibly other inspectiontools that are also coupled to the database. Alternatively, database 54may be configured as a fab database. In this manner, the fab databasemay include inspection results from inspection tool 52 and possiblyother inspection tools that are coupled to the database in addition toinformation generated by other metrology tools, defect review tools,defect characterization tools, processing tools, etc. located within afab and coupled to the fab database. In either example, the inspectiontool may be configured to automatically send the inspection results tothe database upon completion of an inspection process. The inspectiontool may be coupled to the database in any manner known in the art. Inaddition, database 54 may have any suitable configuration known in theart. The inspection results may be further configured as describedherein (e.g., as a KLARF file).

Processor 50 may be configured to access the information in the databasein any manner known in the art. For instance, the processor may becoupled to the database in any manner known in the art. In addition,processor 50 may be configured to receive or determine an identity ofspecimen 56 on which one or more defects are to be reviewed. Processor50 may use the identity of the specimen or any other such information toidentify the corresponding inspection results in database 54.

The inspection results may indicate which defects on the specimen are tobe reviewed. For instance, selection of the defects to be reviewed(“defect sampling”) may be performed by the inspection tool. In thismanner, the defects that are to be reviewed on specimen 56 may beindicated in the inspection results accessed by the processor.Alternatively, defect sampling may be performed by processor 50 based onthe inspection results accessed from the database. Defect sampling maybe performed according to any method known in the art. In this manner,the processor may be configured to identify the defect that is to bereviewed based on the defect sampling results. In addition, inspectiontool 52 or processor 50 may be configured to group the defects selectedfor review into one or more categories prior to the defect reviewprocess. In this manner, the processor may use the categories of thedefect groups to dynamically determine the review run sequence.

Defect review tool 48 also includes imaging subsystem 58 that isconfigured to acquire review image(s) of specimen 56 proximate theposition of the defect indicated in the inspector data. Imagingsubsystem 58 is configured as an optical subsystem in one embodiment.Imaging subsystem 58 is configured to acquire low resolution, large FOVimages of specimen 56. Imaging subsystem 58 may or may not also beconfigured to generate high resolution, small FOV images of specimen 56.Imaging subsystem 58 may be further configured as described herein. Thereview image(s) may include one or more BF images, one or more DFimages, one or more laser DF images, one or more SEM images, or somecombination thereof. As shown in FIG. 11, defect review tool 48 mayinclude stage 60 on which specimen 56 is disposed during imaging byimaging subsystem 58. Stage 60 may include any suitable mechanical orrobotic assembly known in the art.

Processor 50 is also configured to identify a portion of the reviewimage(s) that corresponds to the inspector image(s). The processor maybe configured to identify the portion of the review image(s) thatcorresponds to the inspector image(s) as described herein. For instance,in one embodiment, the processor is configured to identify the portionof the review image(s) that corresponds to the inspector image(s) bycomparing all patterned features illustrated in the inspector image(s)with patterned features illustrated in different portions of the reviewimage(s). In another embodiment, the processor is configured to identifythe portion of the review image(s) that corresponds to the inspectorimage(s) by comparing all patterned features and defect featuresillustrated in the inspector image(s) with patterned features and defectfeatures illustrated in different portions of the review image(s).

In some embodiments, the processor is configured to identify the portionof the review image(s) that corresponds to the inspector image(s) bydetermining if multiple portions of the review image(s) correspond tothe inspector image(s). In one such embodiment, the processor is alsoconfigured to compare the multiple portions with each other at theposition indicated in the inspector data to identify the multipleportion in which the defect is located. In another such embodiment, theinspector data includes additional inspector image(s). In such anembodiment, the processor may be configured to compare the multipleportions with the additional inspector image(s) at the positionillustrated in the additional inspector image(s) to identify themultiple portion in which the defect is located.

Processor 50 is further configured to determine a position of the defectwithin the portion of the review image(s) using the inspector data.Processor 50 may be configured to determine the position of the defectwithin the portion of the review image(s) as described herein. Forinstance, the defect relocation may be performed by correlating theposition of the defect within the patch image from the inspection toolto a position within the low resolution image generated by the imagingsubsystem of the defect review tool.

Defect review tool 48 also includes defect review subsystem 62 that isconfigured as a SEM in one embodiment. The SEM may have any suitableconfiguration known in the art. For example, the SEM may be configuredas one of the e-beam subsystems included in the eDR5000 system, theeCD-1 system, and the eS25 and eS30 systems, which are commerciallyavailable from KLA-Tencor, San Jose, Calif. Electron beam based defectreview subsystems are advantageous for defect review since they providethe highest image quality currently available in addition to highresolution and high contrast images. However, the defect review tool mayinclude any other defect review subsystem known in the art such as ahigh resolution optical imaging subsystem.

As shown in FIG. 11, imaging subsystem 58 and defect review subsystem 62may be disposed within chamber 64. Chamber 64 is preferably a vacuumchamber if defect review subsystem 62 is configured as a SEM. However,in other embodiments, imaging subsystem 58 may be disposed outside ofchamber 64 but within defect review tool 48.

In one embodiment, processor 50 is configured to determine a position ofthe defect with respect to defect review subsystem 62 from the positionof the defect within the portion of the review image(s) such that thedefect can be positioned within a FOV of defect review subsystem 62. Theprocessor may be configured to determine the position of the defect withrespect to defect review subsystem 62 as described herein. In anotherembodiment, the imaging subsystem is configured to acquire additionalreview image(s) at the position of the defect within the portion. In onesuch embodiment, an image type of the review image(s) is different thanan image type of the additional review image(s). In such embodiments,the processor may be configured to verify the position of the defectwithin the portion using the additional review image(s). The additionalreview image(s) may be acquired as described herein.

Defect review tool 48 may be configured to move specimen 56 in direction66 from a position under imaging subsystem 58 such that the defect onspecimen 56 is positioned within a FOV of defect review subsystem 62.For instance, processor 50 may be coupled to stage 60 in any mannerknown in the art. In addition, processor 50 may be configured to controlstage 60 based on the determined position of the defect with respect tothe defect review subsystem such that the defect is positioned withinthe FOV of the defect review subsystem. In this manner, imagingsubsystem 58 and defect review subsystem 62 are coupled to a commonstage.

Processor 50 may be configured to perform any other defect reviewrelated functions known in the art. For instance, processor 50 may beconfigured to classify the defects that are reviewed. In someembodiments, processor 50 may be configured to use more than one imagefor defect classification. In one such embodiment, defect classificationmay be performed using the inspector image(s), the review image(s), theimage(s) generated by the defect review subsystem of the defect reviewtool, or some combination thereof. The processor may also be configuredto use any other information about the defect generated by theinspection tool, the imaging subsystem, and/or the defect reviewsubsystem for defect classification. In addition, the processor may beconfigured to classify defects using any methods and/or algorithms knownin the art. The processor and the defect review tool may also beconfigured for automatic defect classification (ADC).

Processor 50 may also be configured to generate output that includesresults of the defect review process. The output may include, forexample, one or more of the inspector image(s), one or more of thereview image(s), one or more images generated by the defect reviewsubsystem, defect classification information such as defect materialinformation, an optimized or altered recipe for both the inspection tooland/or the defect review tool, or any combination thereof. The outputmay be generated in any format such as a KLARF file or any othersuitable file known in the art.

The defect review tool may also be configured to have additionalfunctionality. For instance, the defect review tool may be configured todetermine a composition of a defect that is reviewed. In one suchexample, the defect review tool may be configured to perform electrondispersive x-ray (EDX) spectroscopy of defects on the specimen. Forinstance, if the defect review subsystem is configured as an electronbeam based subsystem, then the defect review subsystem may also beconfigured to perform EDX. Such a defect review subsystem may have anyappropriate configuration known in the art. Each of the embodiments ofthe defect review tool described above may be further configured asdescribed herein.

An additional embodiment relates to a system configured to locate adefect in a defect review process. One such embodiment of a system isillustrated in FIG. 11. In this embodiment, the system includesinspection tool 52 that is configured to generate inspector image(s)that illustrate an area on a specimen in which a defect to be reviewedis located and inspector data that indicates a position and features ofthe defect within the area. The system also includes defect review tool48 that is configured to acquire the inspector image(s) and theinspector data from inspection tool 52. The defect review tool is alsoconfigured to generate review image(s) of specimen 56 proximate theposition indicated in the inspector data and to identify a portion ofthe review image(s) that corresponds to the inspector image(s). Inaddition, the defect review tool is configured to determine a positionof the defect within the portion of the review image(s) using theinspector data. The system, the inspection tool, and the defect reviewtool may be further configured as described herein. The systemembodiment described above has all of the advantages of the methodembodiments described above.

FIG. 12 illustrates one embodiment of inspection tool 52 that may beincluded in a system as described above. Inspection tool 52 includeslight source 68. Light source 68 may include any appropriate lightsource known in the art. In addition, light source 68 may include abroadband light source, a multiple wavelength (polychromatic) lightsource, or a single wavelength (monochromatic or near-monochromatic)light source. Light from light source 68 is directed to beam splitter70. Beam splitter 70 may include any suitable optical component known inthe art. Beam splitter 70 is configured to direct the light from lightsource 68 to specimen 56. As shown in FIG. 12, beam splitter 70 isconfigured to direct the light from the light source to specimen 56 at asubstantially normal angle of incidence. However, in other embodiments,beam splitter 70 or a different optical component (not shown), which maybe used in place of beam splitter 70, may be configured to direct thelight from light source 68 to specimen 56 at an oblique angle ofincidence. In some embodiments, inspection tool 52 may be configured todirect light from light source 68 to specimen 56 at different angles ofincidence simultaneously or sequentially.

Light reflected from specimen 56 may pass through beam splitter 70 todetection subsystem 72. In this manner, the light that is reflected fromthe specimen is detected by detection subsystem 72. Detection subsystem72 may include any suitable detector known in the art. For example,detection subsystem 72 may include an array detector such as a chargecoupled device (CCD) camera or a time delay integration (TDI) camera. Inthis manner, the detection subsystem may generate output signals 74 thatcan be used to generate an image of specimen 56. In particular,processor 76 may be coupled to detection subsystem 72 in any mannerknown in the art such that the processor can receive output signals 74from detection subsystem 72.

Processor 76 may also be configured to generate images of specimen 56using output signals 74. The images of the specimen generated byprocessor 76 may include inspector image(s) that illustrate an area onthe specimen in which a defect to be reviewed is located. The processormay also be configured to generate inspector data that illustrates aposition and features of the defect within the area. Processor 76 may beconfigured to store the inspector image(s) and the inspector data instorage medium 78. Therefore, upon receiving a request from a defectreview tool (not shown in FIG. 12) for the inspector image(s) and theinspector data, processor 76 may be configured to retrieve the inspectorimage(s) and the inspector data from storage medium 78 and to send theinspector image(s) and the inspector data to the defect review tool.Processor 76 may be coupled to the defect review tool in any mannerknown in the art (e.g., by a transmission medium).

Inspection tool 52 may include any other components known in the art.For example, as shown in FIG. 12, the inspection tool may include stage80 on which specimen 56 is disposed during inspection. Stage 80 mayinclude any suitable mechanical or robotic assembly known in the art.Stage 80 may also be configured to move specimen 56 during inspectionsuch that the light directed to the specimen by beam splitter 70 canscan across the specimen. The inspection tool may also include one ormore additional optical components (not shown). The one or moreadditional optical components may include any suitable opticalcomponents known in the art such as an objective lens, a collector lens,one or more polarizing components, one or more light filteringcomponents, etc.

As described above, inspection tool 52 is configured to detect lightreflected from specimen 56. In this manner, inspection tool 52 isconfigured as a BF inspection tool. In addition, inspection tool 52 isconfigured to generate images of specimen 56. Therefore, inspection tool52 is configured as a BF imaging inspection tool. However, in otherembodiments, inspection tool 52 may be configured to detect lightscattered and/or diffracted from specimen 56. In this manner, inspectiontool 52 may be configured as a DF inspection tool. The DF inspectiontool may also be configured to generate images of the specimen.

In further embodiments, the inspection tool may be configured to inspecta specimen using one or more selectable modes. For instance, theinspection tool may be configured to have BF and DF capabilities, andthe mode that is used to inspect a specimen may be selected based on,for example, characteristics of the specimen and/or characteristics ofthe defect of interest. In another instance, the inspection tool may beconfigured for deep ultraviolet (DUV) BF inspection, ultraviolet (UV) BFinspection, UV DF inspection, or some combination thereof. In addition,the inspection tool may be configured for patterned wafer inspection,bare (or unpatterned) wafer inspection, or both patterned andunpatterned wafer inspection. In an alternative embodiment, theinspection tool may be configured for non-optical inspection. In onesuch embodiment, the inspection tool may be configured as an electronbeam based inspection tool. Such an electron beam based inspection toolmay have any suitable configuration known in the art.

FIG. 13 illustrates one embodiment of an imaging subsystem that may beincluded in the defect review tool embodiments described herein. Thisembodiment of an imaging subsystem may be particularly suitable for aspecimen on which a dense array of patterned structures is formed. Inthis embodiment, imaging subsystem 58 includes an illumination subsystem(not shown) configured to direct light 82 to specimen 56 at obliqueangle of incidence, A. In one example, the illumination subsystem mayinclude a light source such as a relatively high brightness UV source.One example of such a light source may be a laser. However, theillumination subsystem may include any other suitable light source knownin the art. The illumination subsystem may also include any othersuitable optical components known in the art coupled to the lightsource.

As shown in FIG. 13, light 84 is specularly reflected from specimen 56at angle, A, which is equal to the angle of incidence. Light may also bediffracted from patterns on specimen 56. For example, first order light86 diffracted from specimen 56 may propagate from the specimen at adifferent angle than that of the specularly reflected light. Inparticular, the first order light may propagate along a direction at anangle, B, with respect to the specularly reflected light. The angle, B,is proportional to the technology node. In particular, angle, B, tendsto decrease as the technology node decreases. Intensity distribution 88of the light diffracted from specimen 56 at image plane 90 indicates theamount of light scattered from the patterns on the specimen intodifferent orders. Light 92 may also be diffracted by the pattern on thespecimen out of the plane of incidence.

As shown in FIG. 13, imaging subsystem 58 may include detectionsubsystem 94 that is arranged to detect light scattered from specimen56. In some embodiments, the numerical aperture, NA, of the imagingsubsystem may be selected such that the light detected by detectionsubsystem 94 does not include the light reflected from the specimen anda substantial portion of the light diffracted from the specimen. Inother words, the NA of the imaging subsystem may be selected such thatthe detection subsystem does not collect the pattern signal from thespecimen. In this manner, a pattern formed on the specimen may not beresolved in review image(s) formed by detection subsystem 94. Inparticular, as shown in FIG. 13, the NA of the imaging subsystem may beselected such that the detection subsystem collects light diffractedinto only one higher order by the pattern on the specimen. In one suchexample, the NA of the imaging subsystem may be selected to be less than0.8.

By selecting the imaging subsystem NA such that the pattern signal isnot collected, the review image(s) acquired by the imaging subsystem mayhave substantially high defect contrast. In addition, the opticalimaging subsystem shown in FIG. 13 may produce review image(s) havingbetter contrast than a SEM based imaging subsystem since the patternfrom the specimen can be optically filtered by the optical imagingsubsystem. The embodiment of the imaging subsystem shown in FIG. 13 maybe particularly suitable for use as an imaging subsystem of a defectreview tool that is to be used to review defects detected by SEM basedinspection.

Although one particular optical imaging subsystem configuration is shownin FIG. 13, it is to be understood that the optical imaging subsystemcan have a number of different configurations. For example, the opticalimaging subsystem may be configured to use an optical technique such asBF and/or DF optical microscopy on the defect review tool in parallelwith the UV/DUV OM used on the inspection tool. In another example, theoptical imaging subsystem may be configured as a DF OM. Such an opticalimaging subsystem may also be configured to spatially filter light fromthe specimen. The spatial filtering may be Fourier filtering or anyother spatial filtering. In addition, the Fourier or other spatialfiltering may be performed using an analog or digital filter. An analogfilter is a spatial filter that is positioned in the light collectionpath. Digital filtering is performed on the acquired image. In a furtherexample, the optical imaging subsystem may be configured as a UVillumination BF OM capable of high magnification and a relatively highNA (e.g., an NA greater than about 0.9).

As will be understood based on the embodiments described above, a numberof different combinations of inspection tool configurations, imagingsubsystem configurations, and defect review subsystem configurations arewithin the scope of the description provided herein. For instance, inone embodiment, a DUV inspection tool, a SEM imaging subsystem, and aSEM defect review subsystem may be included in a system. In suchembodiments, a SEM imaging subsystem may be used for better resolutionof the features on the specimen. In addition, the imaging subsystem mayalso be used as the defect review subsystem. In these embodiments, thedefect location may be determined by correlating inspector image(s) andinspector data (e.g., one or more patch images and defect information)with review image(s) generated by the SEM imaging subsystem. Such acorrelation may be performed using a block-to-block comparison asdescribed further above. In addition, in such embodiments,classification of the defect may be performed using multiple imagesincluding, for example, the review image(s) in combination with theinspector image(s).

In a different embodiment, a UV BF inspection tool, an optical imagingsubsystem (configured for BF and/or DF (such as Edge Contrast mode inwhich complementary apertures are included in the illumination and lightcollection paths)), and a SEM defect review subsystem may be included ina system. For example, the inspection tool may include one of the 236xor 237x BF inspection tools commercially available from KLA-Tencor, theoptical imaging subsystem may be configured as the AIT Fusion UV systemor PUMA DF system commercially available from KLA-Tencor and possiblyconfigured for visible imaging, and the defect review subsystem may beconfigured as a SEM. In such an embodiment, the defect location may bedetermined by correlating a patch image and defect information generatedby the inspection tool with review image(s) generated by the opticalimaging subsystem. Such a correlation may be performed using ablock-to-block comparison as described further above. In addition, insuch embodiments, classification of the defect may be performed usingmultiple images of the defect including, for example, the SEM imagegenerated by the defect review subsystem, the patch image generated bythe inspection tool, and the image generated by the optical imagingsubsystem.

In another embodiment, a DF imaging inspection tool, an optical imagingsubsystem configured for oblique DF laser based imaging (which mayinclude a relatively high power UV laser), and a SEM defect reviewsubsystem may be included in a system. In such an embodiment, the defectlocation may be determined by correlating images generated by the DFinspection tool and the optical imaging subsystem (possibly incombination with Fourier filtering performed optically orelectronically). In addition, in such embodiments, classification of thedefect may be performed using multiple images of the defect including,for example, the SEM image generated by the defect review subsystem, thepatch image generated by the inspection tool, and the image generated bythe optical imaging subsystem.

In an additional embodiment, a DF inspection tool such as one of the SP1and SP2 tools that are commercially available from KLA-Tencor, anoptical imaging subsystem configured for oblique DF laser based imaging(which may include a relatively high power UV laser), and a SEM defectreview subsystem may be included in a system. In such an embodiment, thedefect location may be determined by correlating images generated by theDF inspection tool and the optical imaging subsystem. In addition, insuch embodiments, classification of the defect may be performed usingmultiple images of the defect including, for example, the SEM imagegenerated by the defect review subsystem and the image generated by theoptical imaging subsystem.

In yet another embodiment, an electron beam based inspection tool suchas the eS3X tool that is commercially available from KLA-Tencor, a SEMimaging subsystem (possibly also configured for voltage contrastimaging), and a SEM defect review subsystem may be included in a system.In such an embodiment, the defect location may be determined bycorrelating a patch image generated by the electron beam basedinspection tool and the image generated by the electron beam imagingsubsystem. Such a correlation may be performed in a block-to-block modeas described further herein. In addition, in such embodiments,classification of the defect may be performed using multiple images ofthe defect including, for example, the patch image generated by theelectron beam based inspection tool and the SEM defect review subsystem.

Although the method and system embodiments are described herein withrespect to locating a defect in a defect review process, it is to beunderstood that the methods and systems can be used to locate a defectin other processes. For instance, the methods and systems describedherein can be used to locate a defect in any other characterization,metrology, and classification processes. In one such example, themethods and systems can be used to locate a defect in an EDX system thatis configured to determine a composition of a defect. In addition, thedefect review tool embodiments described herein can be configured tohave such characterization, metrology, and classification functions.

Further modifications and alternative embodiments of various aspects ofthe invention may be apparent to those skilled in the art in view ofthis description. For example, methods, defect review tools, and systemsfor locating a defect in a defect review process are provided.Accordingly, this description is to be construed as illustrative onlyand is for the purpose of teaching those skilled in the art the generalmanner of carrying out the invention. It is to be understood that theforms of the invention shown and described herein are to be taken as thepresently preferred embodiments. Elements and materials may besubstituted for those illustrated and described herein, parts andprocesses may be reversed, and certain features of the invention may beutilized independently, all as would be apparent to one skilled in theart after having the benefit of this description of the invention.Changes may be made in the elements described herein without departingfrom the spirit and scope of the invention as described in the followingclaims.

1. A method for locating a defect in a defect review process,comprising: acquiring one or more images and data from an inspectiontool, wherein the one or more images illustrate an area on a specimen inwhich a defect to be reviewed is located, and wherein the data indicatesa position and features of the defect within the area; acquiring one ormore additional images of the specimen proximate the position of thedefect indicated in the data using an imaging subsystem of a defectreview tool; identifying a portion of the one or more additional imagesthat corresponds to the one or more images; and determining a positionof the defect within the portion of the one or more additional imagesusing the data.
 2. The method of claim 1, further comprising identifyingthe defect using information within a database, wherein the informationwas generated by the inspection tool.
 3. The method of claim 1, whereinthe one or more images comprise one or more bright field images, one ormore dark field images, one or more laser dark field images, one or morescanning electron microscope images, or some combination thereof.
 4. Themethod of claim 1, wherein the data comprises one or more other imagesthat illustrate the position and the features of the defect within thearea.
 5. The method of claim 1, wherein the one or more additionalimages comprise one or more bright field images, one or more dark fieldimages, one or more laser dark field images, one or more scanningelectron microscope images, or some combination thereof.
 6. The methodof claim 1, wherein said acquiring the one or more images and the datafrom the inspection tool is performed by the defect review tool.
 7. Themethod of claim 1, wherein the defect review tool and the inspectiontool, in combination, are configured as an information on-demand system.8. The method of claim 1, wherein the imaging subsystem is configured asan optical subsystem.
 9. The method of claim 1, wherein the defectreview tool is configured as a scanning electron microscope.
 10. Themethod of claim 1, further comprising determining a position of thedefect with respect to the defect review tool from the position of thedefect within the portion of the one or more additional images such thatthe defect can be positioned within a field of view of the defect reviewtool.
 11. The method of claim 1, wherein the one or more imagesillustrate patterned features on the specimen, and wherein the datacomprises one or more other images that do not illustrate the patternedfeatures on the specimen.
 12. The method of claim 1, wherein saididentifying comprises comparing all patterned features illustrated inthe one or more images with patterned features illustrated in differentportions of the one or more additional images.
 13. The method of claim1, wherein said identifying comprises comparing all patterned featuresand defect features illustrated in the one or more images with patternedfeatures and defect features illustrated in different portions of theone or more additional images.
 14. The method of claim 1, wherein saididentifying comprises determining if multiple portions of the one ormore additional images correspond to the one or more images andcomparing the multiple portions with each other at the positionindicated in the data to identify the multiple portion in which thedefect is located.
 15. The method of claim 1, wherein said identifyingcomprises determining if multiple portions of the one or more additionalimages correspond to the one or more images, wherein the data comprisesone or more other images, and wherein said identifying further comprisescomparing the multiple portions with the one or more other images at theposition of the defect illustrated in the one or more other images toidentify the multiple portion in which the defect is located.
 16. Themethod of claim 1, further comprising verifying the position of thedefect within the portion by acquiring one or more other images at theposition of the defect within the portion, wherein an image type of theone or more additional images is different than an image type of the oneor more other images.
 17. A defect review tool configured to locate adefect in a defect review process, comprising: a processor configured toacquire one or more images and data from an inspection tool, wherein theone or more images illustrate an area on a specimen in which a defect tobe reviewed is located, and wherein the data indicates a position andfeatures of the defect within the area; and an imaging subsystemconfigured to acquire one or more additional images of the specimenproximate the position of the defect indicated in the data; wherein theprocessor is further configured to identify a portion of the one or moreadditional images that corresponds to the one or more images and todetermine a position of the defect within the portion of the one or moreadditional images using the data.
 18. The defect review tool of claim17, wherein the processor is further configured to identify the defectusing information within a database, and wherein the information wasgenerated by the inspection tool.
 19. The defect review tool of claim17, wherein the one or more images comprise one or more bright fieldimages, one or more dark field images, one or more laser dark fieldimages, one or more scanning electron microscope images, or somecombination thereof.
 20. The defect review tool of claim 17, wherein thedata comprises one or more other images that illustrate the position andthe features of the defect within the area.
 21. The defect review toolof claim 17, wherein the one or more additional images comprise one ormore bright field images, one or more dark field images, one or morelaser dark field images, one or more scanning electron microscopeimages, or some combination thereof.
 22. The defect review tool of claim17, wherein the defect review tool and the inspection tool, incombination, are further configured as an information on-demand system.23. The defect review tool of claim 17, wherein the imaging subsystem isconfigured as an optical subsystem.
 24. The defect review tool of claim17, further comprising a defect review subsystem configured as ascanning electron microscope.
 25. The defect review tool of claim 17,wherein the processor is further configured to determine a position ofthe defect with respect to a defect review subsystem from the positionof the defect within the portion of the one or more additional imagessuch that the defect can be positioned within a field of view of thedefect review subsystem.
 26. The defect review tool of claim 17, whereinthe one or more images illustrate patterned features on the specimen,and wherein the data comprises one or more other images that do notillustrate the patterned features on the specimen.
 27. The defect reviewtool of claim 17, wherein the processor is further configured toidentify the portion of the one or more additional images thatcorresponds to the one or more images by comparing all patternedfeatures illustrated in the one or more images with patterned featuresillustrated in different portions of the one or more additional images.28. The defect review tool of claim 17, wherein the processor is furtherconfigured to identify the portion of the one or more additional imagesthat corresponds to the one or more images by comparing all patternedfeatures and defect features illustrated in the one or more images withpatterned features and defect features illustrated in different portionsof the one or more additional images.
 29. The defect review tool ofclaim 17, wherein the processor is further configured to identify theportion of the one or more additional images that corresponds to the oneor more images by determining if multiple portions of the one or moreadditional images correspond to the one or more images and comparing themultiple portions with each other at the position indicated in the datato identify the multiple portion in which the defect is located.
 30. Thedefect review tool of claim 17, wherein the data comprises one or moreother images, and wherein the processor is further configured toidentify the portion of the one or more additional images thatcorresponds to the one or more images by determining if multipleportions of the one or more additional images correspond to the one ormore images and comparing the multiple portions with the one or moreother images at the position illustrated in the one or more other imagesto identify the multiple portion in which the defect is located.
 31. Thedefect review tool of claim 17, the imaging subsystem is furtherconfigured to acquire one or more other images at the position of thedefect within the portion, wherein an image type of the one or moreadditional images is different than an image type of the one or moreother images, and wherein the processor is further configured to verifythe position of the defect within the portion using the one or moreother images.
 32. A system configured to locate a defect in a defectreview process, comprising: an inspection tool configured to generateone or more images that illustrate an area on a specimen in which adefect to be reviewed is located and data that indicates a position andfeatures of the defect within the area; and a defect review toolconfigured to acquire the one or more images and the data from theinspection tool, to generate one or more additional images of thespecimen proximate the position indicated in the data, to identify aportion of the one or more additional images that corresponds to the oneor more images, and to determine a position of the defect within theportion of the one or more additional images using the data.